This project will develop a decision-making tool to help AIS managers, counties, and other agencies prioritize their resources for optimal prevention and intervention of AIS, specifically zebra mussels and starry stonewort. The tool will answer two major questions:
- Can it get here? To assess this risk, researchers will take into account the geographic proximity to an infested lake, boater movement in Minnesota, and water connectivity.
- Can it survive here? This will be answered using species-specific ecological niche models. These suitability models take into account lake and landscape variables such as temperature, precipitation, pH, conductivity, and chlorophyll.
A static version of this model has already been created by a previous MAISRC project. This new model will take that, integrate new data, and build it so it can incorporate up-to-the-minute changes. Once input from counties and other stakeholders is taken into account and the model is finalized, it will be put online in a user-friendly format for AIS managers and agencies to use.
Once the decision optimization model is created, reports will be created and distributed to counties to help them prioritize their resource allocations in order to have the biggest impact on reducing the risk of spread of AIS.
Preventing the spread of AIS through human-associated pathways is a priority for many state and local agencies. A science-based tool to inform planning and decision-making is urgently needed.
The first step of developing AIS risk estimates for each lake in Minnesota is completed, with the creation of a hydromorphological network model. The model suggests that while water connectivity is important, other factors are also clearly influencing the spread of AIS. Researchers have now begun to evaluate optimal management scenarios based on the data available for lake connectivity and suitability.
We used a big data approach to combine hydrologic connectivity and boat movement to create a multiplex metacommunity model for both zebra mussel and Eurasian watermilfoil. We found that the hydrological corridors are important pathways of spread, even more so that previous research has suggested. While overland dispersal of AIS via boater movement is still a significant factor, additional management strategies should be developed to include intervention of hydrological pathways.
Using connectivity networks of boater movement, we developed county-based AIS management optimization models that prioritize inspection locations that will intercept the highest number of ‘risky boats’ (e.g. moving from infested to uninfested lakes). We piloted the models in Crow Wing, Ramsey, and Stearns Counties and had a very productive collaboration with county managers and citizen advisory boards during the development and evaluation for each. Ultimately, the application of this approach was well received and helped inform allocation of their inspection hours at the county level (for example, Crow Wing County).
Dissemination and usability of the models was a priority of this project. We created online tools to 1) visualize the spread risk for zebra mussels and Eurasian watermilfoil based on model predictions made in Activity 1, and 2) visualize and modify the decision optimization model at the county level based on management thresholds or funding availability.